Systems and methods for recommending collaborative content

ABSTRACT

The system generates a recommendation of content for use in collaboration, allowing relevant content to be used as base content. The system identifies a content item, and retrieves reviews for the content item from one or more sources or forums. The system filters the reviews to generate a reduced set of reviews based on text of the respective reviews, profile information associated with the reviews, and reference information. A recommendation metric is determined for the content item based on the reduced set of reviews and based on the one or more recommendation criteria. The recommendation criteria specify which aspects of the content impact recommendation, and how those aspects impact recommendation. The recommendation metric indicates whether the content item is recommended as base content, to be used for generating collaborative content. The system generates a recommendation indicator indicative of the recommendation metric, and outputs the indicator for display, storage, or both.

BACKGROUND

The present disclosure relates to devices providing contentrecommendations, and, more particularly, devices that providerecommendations of original content, base content, and collaborativecontent.

SUMMARY

In a platform that provides collaborative content creation, base contentis media content on which multiple collaborations can be built. Thisbase content is created by a user based on some original content. Forexample, the original content can be a song from soundtrack, movie,show, or album. While the original content may be well known among manyusers, there may be a significant number of fans or critics who haveopinions about how the content could have been better composed orperformed. Such opinions may be found in forums such as social mediapages and may refer to properties of the content. In the context of asong, the reviews may refer to voice suitability, pitch, notes, lyrics,rhythm, tempo, usage of musical instruments, light effects, climaticsettings, or other properties. These critical reviews help the creatorof base content create rich and relevant base content variations ascompared to the original soundtrack.

The present disclosure describes systems and methods that generate arecommendation as to which original content, and improvements thereto,may be of interest to a base content creator. Reviews from one or moreforums, such as social media platforms, are collected and curated intocategories. For example, for a song, the categories may include pitch,lyrics, musical instruments, special effects, or other properties, andthe corresponding one or more recommendations are made available to thebase content creator at the time of base content creation. A contentitem and a plurality of reviews referring to the content item from oneor more databases are retrieved, along with associated metadata. Themetadata includes profile information associated with the reviews, thereviewers, or both. The retrieved reviews are filtered based onpredetermined categories, text of the reviews as compared to keywords,associated reviewers, ratings of the reviews, popularity of the reviews,or other filter settings. Based on recommendation criteria such as whichcategories to provide recommendations for, and how to determinerecommendations for each category, the system determines arecommendation metric for the content item based on the filteredreviews. The recommendation metric may be outputted directly, forstorage, display, or both, or an indicator indicative of therecommendation metric may be outputted. The outputted recommendationprovides the base content creator with guidance towards which content issuitable for conversion to base content, and what that conversion shouldentail.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and advantages of the disclosure will beapparent upon consideration of the following detailed description, takenin conjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows a block diagram of an illustrative system for recommendingoriginal content, in accordance with some embodiments of the presentdisclosure;

FIG. 2 shows a block diagram of an illustrative system for recommendingbase content, in accordance with some embodiments of the presentdisclosure;

FIG. 3 shows a block diagram of an illustrative system for recommendingcollaborative content, in accordance with some embodiments of thepresent disclosure;

FIG. 4 is a block diagram of an illustrative user equipment, inaccordance with some embodiments of the present disclosure;

FIG. 5 is a block diagram of an illustrative system for providingrecommendations, in accordance with some embodiments of the presentdisclosure;

FIG. 6 is a flowchart of an illustrative process for generating arecommendation of original content, in accordance with some embodimentsof the present disclosure;

FIG. 7 is a flowchart of an illustrative process for generating arecommendation of a media content item, in accordance with someembodiments of the present disclosure; and

FIG. 8 is a flowchart of an illustrative process for generating arecommendation of collaborative content, in accordance with someembodiments of the present disclosure.

DETAILED DESCRIPTION

Collaborative content may be created by adding layers to existingcontent. For example, a collaboration may include a karaoke file, inwhich a user sings over a base track (e.g., which may but need notinclude vocals). Base content, which is used as a base to generatecollaborative content, is derived from original content. The selectionof original content (e.g., as a candidate for collaboration), selectionof base content (e.g., for collaboration), and selection ofcollaborative content (e.g., for consumption) may be aided byrecommendations. FIGS. 1-3 show illustrative systems for recommendingcontent in the context of collaboration.

FIG. 1 shows a block diagram of illustrative system 100 for recommendingoriginal content, in accordance with some embodiments of the presentdisclosure. System 100 may be configured to, for example, identifyoriginal content, and provide recommendations of original content basedon reviews, profile information, and properties of the content. In someembodiments, an application that provides collaborative content creationis implemented using system 100. Base content is content upon whichmultiple collaborative content items may be built. This base content iscreated by an entity from an original content, which was created as afinished content item (e.g., without regard to collaboration). Forexample, in the context of music, base content may be laid over originalcontent such as an original soundtrack for a movie, a show, or an album.While the original soundtrack may be popular with the masses, there mayexist a significant number of music or content lovers who have opinionsabout how the soundtrack could have been composed better, or how tocollaborate with the content. For example, numerous such reviews may befound in social media referring to voice suitability, pitch, notes,lyrics, rhythm, tempo, usage of musical instruments, usage of lighteffects, climatic settings, or any other aspect of the original content.These critical reviews could help a creator of base content create arich and unique base content, possibly with several variations ascompared to the original soundtrack. These reviews from various socialmedia platforms or other forums can be collected and curated intocategories such as, for example, pitch, lyrics, musical instrumentsincluded, special effects, or any other category, and made available tothe entity at the time of base content creation. The entity may include,for example, an end user (e.g., and associated profile thereof), abusiness entity, a social media entity, an entity associated with anapplication, a website, or a hardware device.

System 100 includes recommendation engine 120, which includes contentanalyzer 121, review analyzer 122, and profile analyzer 123, asillustrated. Recommendation engine 120 may receive information from oneor more databases 170, user profile information 160, referenceinformation 150, any other suitable information, or any combinationthereof. Recommendation engine 120 may receive reviews 130, reviewinformation 131, content 132 and metadata thereof, or any combinationthereof. Recommendation engine 120 may store information inrecommendation database 171, provide recommendations 180, providecontent 181, perform tag management 182, provide any other suitableoutput, or perform any combination thereof.

Recommendation engine 120 is configured to retrieve and gather reviews130 of an original content item from social media websites, social mediaapplications, content-focused applications, or any other suitable forum.Recommendation engine 120 is also configured to analyze these reviews toprovide categorized recommendations (e.g., recommendations 180) to abase content creator. For example, recommendation engine 120 may collectreviews 130 from social media applications (e.g., Facebook, Twitter,Instagram and other social media sites) for an original content item,and then filter the reviews based on suitable criteria (e.g., hashtagsassociated with the content). In a further example, recommendationengine 120 may collect review information 131 from social mediaapplications (e.g., Facebook, Twitter, Instagram and other social mediasites) associated with reviews 130. Review information 131 may includemetadata, entity identifiers, uniform resource identifiers or locators,profile information associated with a reviewer, comments or feedbackdirected at a review, any other suitable information about a review orreviewer, or any combination thereof. In some embodiments, reviews 130and review information 131 may be combined (e.g., stored in datatogether, linked with each other), and accordingly review information131 need not be included separately. In some embodiments, recommendationengine 120 identifies reviews that provide suggestions of improvementsof original content. For example, recommendation engine 120 may searchfor keywords such as pitch, voice, better, tempo, names of musicalinstruments, or other keywords or phrases indicative of a review,opinion, or suggestion. In some embodiments, recommendation engine 120curates reviews 130 to provide the base content creator withrecommendation to create one or more base content items based on theoriginal content.

Recommendations may be categorized by format, genre, one or more contentproperties, collaborator attributes (e.g., prospective or likelycollaborators), reviewer attributes, popularity, changes, or any othersuitable category. To illustrate, a recommendation may include whether acontent item is recommended, for what the content item is recommended,what changes to the content item are recommended, any other suitablerecommendation, or any combination thereof. Recommendations of acategory may be based on the number of reviews in the category, thenumber of users providing reviews relevant to the category, the numberof reactions to the reviews on social media, profile information of thereviewer (e.g., review information, user profile information, or both,and relevance thereof), any other suitable information, or anycombination thereof. In an illustrative example, each recommendationtype for a specific original content item can be weighted by the numberof users providing the feedback, a number of likes on the feedback, therelevance of the user or entity giving the feedback, or a combinationthereof. For example, for original content that includes an originalsoundtrack or songs thereof, a review provided by a user who has a musicbackground will have a higher weightage compared to a review from a usernot having a music background. Recommendation engine 120 may store oneor more recommendations 180 in recommendation database 171, which mayinclude one or more databases. For example, recommendation database 171may store a curated set of recommendations for each original contentitem that is based on reviews retrieved and gathered from various socialmedia platforms. In a further example, recommendation engine 120 mayretrieve each review and assign a score. The set of recommended changeswith the highest score will be presented to the base content creatorwhen they select a given content item. For example, by using reviewsfrom users, a base content creator may provide base content that isrelevant for the users, and thus encourages collaboration. In someembodiments, recommendations for one or more recommendation categoriescorrespond to recommended modifications to the original content togenerate respective base content. For example, if recommendation engine120 identifies three categories from the reviews (e.g., pitch, tempo,lighting), recommendation engine 120 may generate three correspondingrecommendation indicators. In response, for example, a base contentcreator may generate three versions of base content from the originalcontent, each including a respective modification (e.g., modified pitch,modified tempo, modified lighting).

User profile information 160 may include profile information associatedwith a base content creator, reviewer, or any other suitable entity. Forexample, user profile information 160 may include identifiers (e.g.,names, usernames, nicknames, identification numbers), search history(e.g., fired queries), content consumption history (e.g., videos viewed,audio content listened to, content interacted with), any other suitableuser information, or any combination thereof. For example, the userinformation may include current and/or historical user activityinformation (e.g., what communications the user engages in, what timesof day the user consumes content, whether the user interacts with asocial network, at what times the user interacts with a social networkto post reviews or comments), what types of content the user typicallyconsumes, stored contacts of the user, frequent contacts of the user,any other suitable information, or any combination thereof. In someembodiments, the user information may identify patterns of a given userfor a period of more than one year.

Reference information 150 includes any suitable information that may beused as a reference to generate one or more recommendations 180. Forexample, reference information 150 may include reference keywords andreference templates for determining which reviews are relevant, and forwhich category of recommendation the review may be applied.

In an illustrative example, recommendation engine 120 may retrievereviews 130 (e.g., from a social media forum) associated with aplurality of users. Recommendation engine 120 may analyze reviews 130 toidentify referenced content items (e.g., using entity recognition,keywords, hashtags, or other identifiers). In some embodiments, allidentified content items are analyzed for recommendation. In someembodiments, identified content items having a plurality of associatedreviews (e.g., at least a predetermined number of reviews) may beanalyzed for recommendation. Recommendation engine 120 may collectreviews of reviews 130 associated with the content item, and identifyfurther content items.

Table 1 shows illustrative recommendation categories, including contentproperties, popularity (e.g., popularity of content, reviews, orreviewers), and user information (e.g., prospective consumers or basecontent creators). In some embodiments, content properties may includesub-categories (e.g., such as pitch, tempo, improvement thereof), forwhich each has an associated recommendation metric. In some embodiments,the recommendation metrics may be combined to generate a singlerecommendation metric. In some embodiments, popularity may includesub-categories (e.g., such as content popularity, review popularity,reviewer popularity), for which each has an associated recommendationmetric. For example, a content item may be trending among social mediaplatforms, and an associated popularity-based recommendation metric mayaccordingly be stronger. In a further example, a reviewer may be highlyviewed and rated, and an associated popularity-based recommendationmetric may be stronger. In some embodiments, user information mayinclude sub-categories (e.g., such as user preferences, favoriteattributes, genre), for which each has an associated recommendationmetric. For example, recommendation engine 120 may specify categoriesfor country songs or slow songs, and recommend content items that fitthose descriptions more strongly to a base content creator whoseassociated profile information indicates those preferences.

TABLE 1 Categories of recommendation Category Keywords/CategoriesTemplate Content {pitch;tempo;voice;better; Identify keywords →properties {instruments};range;notes; Categorize lyrics;effects;rhythm;harmony;settings} Popularity {trending, viral, most, Identify keywords →likes} Determine statistics → Categorize User{{preferences};{favorites}; Identify keywords → Information {genre}}Categorize

Panel 190 shows an illustrative display of identifiers for content items191 and 195, and associated recommendation indicators 192, 193, and 196.Recommendation indicators 192 and 193 correspond to recommendations intwo recommendation categories (e.g., pitch and popularity, or highlyrated and compatible genres) for content item 191. Recommendationindicator 196 corresponds to a single recommendation for content item191. Recommendation indicator 196 may be derived from a singlerecommendation category, or be a composite recommendation based on aplurality of recommendation categories. A base content creator may viewthe display of panel 190 and select the content item having the mostrelevant or preferred recommendations.

FIG. 2 shows a block diagram of illustrative system 200 for recommendingbase content, in accordance with some embodiments of the presentdisclosure. Users may access a variety of platforms to interact withcontent, and within a given platform the intentions of a user can bevaried at different times. The content may include video content, audiocontent, image content, any other suitable content, or any combinationthereof. In some embodiments, system 200 provides varied recommendationsto the same user. For example, if the user wants to sing along to a song(e.g., as karaoke), system 200 presents content to that user thatincludes music they can easily or effectively sing, which may differfrom music the user usually listens to. In some embodiments, system 200determines how the user is planning to use the platform and providessuitable recommendations of content. In an illustrative example, a usermay enjoy listening to a high pitched song but does not desire to singalong or attempt to collaborate on that song. In a further illustrativeexample, a user may desire to dance along to a break-dancing video, andbe capable of such moves, although the user does not usually watchbreakdancing videos. System 200 provides recommendations of content thatis appropriate for the user's performance in terms of skill, preference,and suitability.

System 200 includes recommendation engine 220, which includes propertyanalyzer 221 and profile analyzer 222, as illustrated. Recommendationengine 220 may receive information from one or more databases 270, userprofile information 260, reference information 250, any other suitableinformation, or any combination thereof. Recommendation engine 220 mayretrieve one or more media content items 230, which may include basecontent. Recommendation engine 220 may store information inrecommendation database 271, provide recommendations 280, providecontent 281, perform tag management 282, provide any other suitableoutput, or perform any combination thereof.

Recommendation engine 220 may determine parameters such as how the userentered the system (e.g., a login, immediately previous viewings, orlinks). For example, recommendation engine 220 may determine that if auser accesses an application using a shortcut on a user device, then theuser has an intention to perform (e.g., sing, dance, move, draw). In afurther example, recommendation engine 220 may determine that if theuser accesses the application using a shared link or notification on asocial media, then the intention may be based on properties of the link.For example, if the link is to join someone else to sing, the intentionis to sing. In a further example, if the link is to listen to a song(e.g., media content item 230), then the intention is to listen (e.g.,and not sing). Recommendation engine 220 may respond to a user inputbased on the input itself. For example, recommendation engine 220 maydetermine that a content item is to be consumed as a defaultdetermination (e.g., if no microphone is connected to a user device). Ina further example, recommendation engine 220 may determine that thecontent is to be played, and a user's performance is to be recorded(e.g., which may be collaborative content) based on user location (e.g.,the user is in a location usually used to record such as a home studioenvironment), based on a connected device (e.g., a set of headphones),or based on other suitable criteria. In some embodiments, recommendationengine 220 uses these parameters to determine which content to recommendfor consumption, collaboration, performance, or a combination thereof.

In an illustrative example, if recommendation engine 220 determines thatthe intention is to perform, then recommendation engine 220 may identifyone or more types of songs that the user can sing well or otherwiselikes to sing (e.g., based on user profile information 260). Forexample, recommendation engine 220 may identify songs by pitch (e.g.,low or high), speed (e.g., fast tempo, slow tempo), ratings (e.g.,stored in user profile information 260 from previous performances),invites (e.g., from other users, that may specify a media content item),consumption history (e.g., known or previously consumed songs from oneor more platforms, as stored in user profile information 260), any othersuitable criteria, or any combination thereof. In some embodiments,recommendation engine 220 uses analysis of the user's capabilities tomatch them with media content items 230 (e.g., which may be stored inone or more databases 270) that have similar or suitablecharacteristics.

In a further illustrative example, recommendation engine 220 maydetermine that the intention is to consume but not perform (e.g.,listen, watch, or both) based on consumption history or preferences(e.g., of user profile information 260). In some embodiments,recommendation engine 220 recommends a media content item or otherwisebases a recommendation on a rating of the content across one or moreplatforms. For example, a media content item having a highest rating(e.g., 5 out of 5 stars) may be more strongly recommended byrecommendation engine 220.

Property analyzer 221 is configured to analyze media content,performances, and properties thereof to be used for makingrecommendations. For example, in the context of a karaoke-based musicalcomposition, content analyzer 221 may determine how well the contentmatches reference criteria (e.g., of user profile information 260) suchas the user's ability to match pitch, tempo, harmony, volume, or otheraudio property. In a further example, comparisons against the referencecriteria may be considered to be metrics for good audio quality or anotherwise well-formed performance. In some embodiments, content analyzer221 analyzes the media content item in the time domain, spectral domain,any other suitable domain, or any combination thereof to determineproperties. In some embodiments, content analyzer 221 uses referenceinformation 250 to compare against properties, or metrics determinedthereof, of the media content item. For example, templates for anysuitable property may be stored in reference information 250, andretrieved for comparison by recommendation engine 220.

Profile analyzer 222 is configured to compare user profile information260 to properties of media content item 230, or associated metadata tagsthereof. For example, user profile information 260 may includeconsumption history (e.g., which content the user has watched orlistened to), performance history (e.g., which content the user hasinteracted with, what performance components the user has achieved, andassociated statistical information), ability-focused tags (e.g.,achievable pitches, achievable moves), any other information about theuser and their behavior, or any combination thereof.

In an illustrative example, a user may access a recommendationapplication (e.g., recommendation engine 220) on a smartphone. The usermay indicate an intention to perform karaoke by selecting a soft buttondisplayed by the recommendation application. In response, therecommendation application may identify a song that has associated tagsthat match tags of profile information associated with the user. If theprofile information indicates that the user prefers rock and roll songs,recommendation engine 220 may recommend a rock and roll song thatincludes a pitch range and tempo that match the user's capabilities(e.g., as indicated in user profile information 260).

Panel 290 shows an illustrative display of identifiers for a user'sprofile 291, and base content items 295, 296, and 297. Recommendationindicator 292 is displayed associated with base content 296, which ismost highly recommended. Icon 293 includes a link to play, display, orotherwise provide base content 296 for consumption or collaboration.Identifier 294 corresponds to the user, and may be associated with userprofile information 260 associated with the user.

FIG. 3 shows a block diagram of illustrative system 300 for recommendingcollaborative content, in accordance with some embodiments of thepresent disclosure. System 300 may be configured to, for example,identify collaborative content, and provide recommendations ofcollaborative content based on properties of the content, comparativeproperties of the content relative to reference content, user profileinformation (e.g., of the base content creator or collaborator),compatibility of content creators, a format of the content, popularityinformation (e.g., of the collaboration, associated base content orassociated original content), any other suitable considerations, or anycombination thereof. In some embodiments, an application that providescollaborative content creation is implemented using system 300.Collaborative content, or a “collaboration,” may be created by addingadditional content to base content. The additional content may beoverlaid, blended, or otherwise combined with the base content. Forexample, multiple users can each create a replica of a base contentitem, which is created by a user (e.g., a base content creator). In afurther example, the creator of the base content can review the combinedcontent where other users have collaborated with that base content. Insome instances, the base content is viral or trending, and the basecontent creator of this content will have hundreds of users interactwith the base content, follow a social media feed, provide comments andreviews, or otherwise interact the base content and creator. In afurther example, viewers or other users who want to interact with orconsume content may create a large number of collaborations to choosefrom. Because parsing and selecting among the large number of contentitems may be arduous, and increasingly time consuming as the number ofusers grows, system 300 provides recommendations for collaborations. Toillustrate, if hundreds of users joined a collaboration, eachcollaborating in some way, the resulting collaborative content wouldlikely be too numerous for a user to navigate by themselves (e.g., theuser might not have time to consume and compare that number of contentitems). System 300 provides means to recommend top collaborations withthe base content (e.g., for the creator to review and consume, or forother users).

System 300 includes recommendation engine 320, which includes contentanalyzer 321, audio analyzer 322, and profile analyzer 323, asillustrated. Recommendation engine 320 may receive information from oneor more databases 370, user profile information 360, referenceinformation 350, any other suitable information, or any combinationthereof. Recommendation engine 320 may retrieve original content 330,base content 331, collaborative content 332, information associated withcontent, or a combination thereof. Recommendation engine 320 may storeinformation in recommendation database 371, provide recommendations 380,provide content 381, perform tag management 382, provide any othersuitable output, or perform any combination thereof.

Content analyzer 321 is configured to analyze collaborative content andproperties thereof to be used for making recommendations. For example,in the context of a karaoke collaboration, content analyzer 321 maydetermine how well the content matches reference criteria (e.g., ofreference information 350) such as pitch, tempo, harmony, volume, or anyother audio property. In a further example, comparisons against thereference criteria may be considered to be metrics for good audioquality or an otherwise well-formed collaboration. Content analyzer 321may analyze the media content item in the time domain to determinetemporal information (e.g., phasing, pauses, lengths), spectral domainto determine frequency information (e.g., to determine a note, phases,frequency), any other suitable domain (e.g., using a wavelet transformor other time-frequency transform), or any combination thereof. In someembodiments, content analyzer 321 uses reference information 350 tocompare against properties, or metrics determined thereof, of the mediacontent item. For example, templates for any suitable property may bestored in reference information 350 and retrieved for comparison byrecommendation engine 320.

In some embodiments, recommendation engine 320 bases its recommendationon a perfection metric of the collaboration content (e.g., properties ofthe collaborative content compared against a reference). For example,recommendation engine 320 may determine if the created collaborationmatches some or all aspects of pitch and tempo of the base content. Insome embodiments, recommendation engine 320 bases its recommendation ona compatibility of content creators (e.g., the base content creator andthe collaborator). In some embodiments, recommendation engine 320 basesits recommendation on property comparisons (e.g., pitch, harmony, orother property) such that the overall content together is better toconsume. In some embodiments, recommendation engine 320 bases itsrecommendation on ratings of the collaborator, and optionally ratings ofthe base content creator. For example, if the base content is associatedwith an average rating with average performance indicators (e.g.,conformance to pitch and rhythm), recommendation engine 320 mayrecommend collaborative content having creators with similar ratings(e.g., with more highly rated singers being more strongly recommended).In some embodiments, recommendation engine 320 bases its recommendationon the format of the base content and collaborative content. Forexample, if the base content was created as a video, preference (e.g., astronger recommendation) is given to collaborative content includingvideo. A recommendation may be based on metrics and parameters derivedusing audio, video, image, spectral, temporal, or other analysis of basecontent, collaborative content, original content, or a combinationthereof as inputs, with the analysis results stored in a suitabledatabase for further analysis.

Panel 390 shows an illustrative display of identifiers for base content391, and collaborations 395, 396, and 397. Recommendation indicator 392is displayed associated with collaboration 396, which is most highlyrecommended. Icon 393 includes a link to play, display, or otherwiseprovide collaboration 396 for consumption. Identifier 394 corresponds tothe creator of base content 391, and may be associated with user profileinformation 360 for a creator of base content 391.

In some embodiments, a recommendation application or collaborationapplication may include the functionality of any or all ofrecommendation engines 120, 220, and 320 of FIGS. 1-3. For example, anapplication may be configured to provide recommendations to base contentcreators, collaborators, and other users for whom content may be ofinterest. It will be understood that components or functionality ofrecommendation engines 120, 220, and 320 of FIGS. 1-3 may be combined,omitted, appended, or otherwise modified in accordance with the presentdisclosure.

A user may access content, an application (e.g., for providingrecommendations, content, or both), and other features from one or moreof their devices (i.e., user equipment or audio equipment), one or morenetwork-connected devices, one or more electronic devices having adisplay, or a combination thereof, for example. Any of the illustrativetechniques of the present disclosure may be implemented by a userdevice, a device providing a display to a user, or any other suitablecontrol circuitry configured to respond to a voice query and generate adisplay of content to a user.

FIG. 4 shows generalized embodiments of an illustrative user device.User equipment system 401 may include set-top box 416 that includes, oris communicatively coupled to, display 412, audio equipment 414, anduser input interface 410. In some embodiments, display 412 may include atelevision display or a computer display. In some embodiments, userinput interface 410 is a remote-control device. Set-top box 416 mayinclude one or more circuit boards. In some embodiments, the one or morecircuit boards include processing circuitry, control circuitry, andstorage (e.g., RAM, ROM, hard disk, removable disk, etc.). In someembodiments, circuit boards include an input/output path. Each one ofuser device 400 and user equipment system 401 may receive content anddata via input/output (hereinafter “I/O”) path 402. I/O path 402 mayprovide content and data to control circuitry 404, which includesprocessing circuitry 406 and storage 408. Control circuitry 404 may beused to send and receive commands, requests, and other suitable datausing I/O path 402. I/O path 402 may connect control circuitry 404 (andspecifically processing circuitry 406) to one or more communicationspaths (described below). I/O functions may be provided by one or more ofthese communications paths but are shown as a single path in FIG. 4 toavoid overcomplicating the drawing. While set-top box 416 is shown inFIG. 4 for illustration, any suitable computing device having processingcircuitry, control circuitry, and storage may be used in accordance withthe present disclosure. For example, set-top box 416 may be replaced by,or complemented by, a personal computer (e.g., a notebook, a laptop, adesktop), a network-based server hosting a user-accessible clientdevice, a non-user-owned device, any other suitable device, or anycombination thereof.

Control circuitry 404 may be based on any suitable processing circuitrysuch as processing circuitry 406. As referred to herein, processingcircuitry should be understood to mean circuitry based on one or moremicroprocessors, microcontrollers, digital signal processors,programmable logic devices, field-programmable gate arrays (FPGAs),application-specific integrated circuits (ASICs), etc., and may includea multi-core processor (e.g., dual-core, quad-core, hexa-core, or anysuitable number of cores) or supercomputer. In some embodiments,processing circuitry is distributed across multiple separate processorsor processing units, for example, multiple of the same type ofprocessing units (e.g., two Intel Core i7 processors) or multipledifferent processors (e.g., an Intel Core i5 processor and an Intel Corei7 processor). In some embodiments, control circuitry 404 executesinstructions for an application stored in memory (e.g., storage 408).Specifically, control circuitry 404 may be instructed by the applicationto perform the functions discussed above and below. For example, theapplication may provide instructions to control circuitry 404 togenerate media guidance displays, collaboration application displays, orother suitable displays. In some implementations, any action performedby control circuitry 404 may be based on instructions received from theapplication.

In some client/server-based embodiments, control circuitry 404 includescommunications circuitry suitable for communicating with an applicationserver or other networks or servers. The instructions for carrying outthe above-mentioned functionality may be stored on the applicationserver. Communications circuitry may include a cable modem, anintegrated-services digital network (ISDN) modem, a digital subscriberline (DSL) modem, a telephone modem, an ethernet card, or a wirelessmodem for communications with other equipment, or any other suitablecommunications circuitry. Such communications may involve the Internetor any other suitable communications networks or paths. In addition,communications circuitry may include circuitry that enables peer-to-peercommunication of user equipment devices, or communication of userequipment devices in locations remote from each other (described in moredetail below).

Memory may be an electronic storage device such as storage 408 that ispart of control circuitry 404. As referred to herein, the phrase“electronic storage device” or “storage device” should be understood tomean any device for storing electronic data, computer software, orfirmware, such as random-access memory, read-only memory, hard drives,optical drives, solid state devices, quantum storage devices, gamingconsoles, gaming media, any other suitable fixed or removable storagedevices, and/or any combination of the same. Storage 408 may be used tostore various types of content described herein as well as mediaguidance data described above. Nonvolatile memory may also be used(e.g., to launch a boot-up routine and other instructions). Cloud-basedstorage, for example, may be used to supplement storage 408 or insteadof storage 408.

A user may send instructions to control circuitry 404 using user inputinterface 410. User input interface 410, display 412, or both mayinclude a touchscreen configured to provide a display and receive hapticinput. For example, the touchscreen may be configured to receive hapticinput from a finger, a stylus, or both. In some embodiments, user device400 may include a front-facing screen and a rear-facing screen, multiplefront screens, or multiple angled screens. In some embodiments, userinput interface 410 includes a remote-control device having one or moremicrophones, buttons, keypads, any other components configured toreceive user input, or combinations thereof. For example, user inputinterface 410 may include a handheld remote-control device having analphanumeric keypad and option buttons. In a further example, user inputinterface 410 may include a handheld remote-control device having amicrophone and control circuitry configured to receive and identifyvoice commands and transmit information to set-top box 416.

Audio equipment 414 may be provided as integrated with other elements ofeach one of user device 400 and user equipment system 401 or may bestand-alone units. The audio component of videos and other contentdisplayed on display 412 may be played through speakers of audioequipment 414. In some embodiments, the audio may be distributed to areceiver (not shown), which processes and outputs the audio via speakersof audio equipment 414. In some embodiments, for example, controlcircuitry 404 is configured to provide audio cues to a user, or otheraudio feedback to a user, using speakers of audio equipment 414. Audioequipment 414 may include a microphone configured to receive audio inputsuch as voice commands and speech (e.g., including voice queries). Forexample, a user may speak letters or words that are received by themicrophone and converted to text by control circuitry 404. In a furtherexample, a user may voice commands that are received by the microphoneand recognized by control circuitry 404.

In some embodiments, user device 400, user equipment system 401, or bothmay be used to implement an application for providing recommendations,managing collaborations, or performing other suitable actions. Theapplication may be implemented using any suitable architecture. Forexample, a stand-alone application may be wholly implemented on each oneof user device 400 and user equipment system 401. In some suchembodiments, instructions for the application are stored locally (e.g.,in storage 408), and data for use by the application is downloaded on aperiodic basis (e.g., from an out-of-band feed, from an Internetresource, or using another suitable approach). Control circuitry 404 mayretrieve instructions for the application from storage 408 and processthe instructions to generate any of the displays discussed herein. Basedon the processed instructions, control circuitry 404 may determine whataction to perform when input is received from input interface 410. Forexample, movement of a cursor on a display up/down may be indicated bythe processed instructions when input interface 410 indicates that anup/down button was selected. The application and/or any instructions forperforming any of the embodiments discussed herein may be encoded oncomputer-readable media. Computer-readable media includes any mediacapable of storing data. The computer-readable media may be transitory,including, but not limited to, propagating electrical or electromagneticsignals, or may be non-transitory including, but not limited to,volatile and non-volatile computer memory or storage devices such as ahard disk, floppy disk, USB drive, DVD, CD, media card, register memory,processor cache, Random Access Memory (RAM), etc.

In some embodiments, the application is a client/server-basedapplication. Data for use by a thick or thin client implemented on eachone of user device 400 and user equipment system 401 is retrieved ondemand by issuing requests to a server remote from each one of userdevice 400 and user equipment system 401. For example, the remote servermay store the instructions for the application in a storage device. Theremote server may process the stored instructions using circuitry (e.g.,control circuitry 404) and generate the displays discussed above andbelow. The client device may receive the displays generated by theremote server and may display the content of the displays locally onuser device 400. This way, the processing of the instructions isperformed remotely by the server while the resulting displays, which mayinclude text, a keyboard, or other visuals, are provided locally on userdevice 400. User device 400 may receive inputs from the user via inputinterface 410 and transmit those inputs to the remote server forprocessing and generating the corresponding displays. For example, userdevice 400 may transmit a communication to the remote server indicatingthat an up/down button was selected via input interface 410. The remoteserver may process instructions in accordance with that input andgenerate a display of the application corresponding to the input (e.g.,a display that moves a cursor up/down). The generated display is thentransmitted to user device 400 for presentation to the user.

In some embodiments, the application is downloaded and interpreted orotherwise run by an interpreter or virtual machine (e.g., run by controlcircuitry 404). In some embodiments, the application may be encoded inthe ETV Binary Interchange Format (EBIF), received by control circuitry404 as part of a suitable feed, and interpreted by a user agent runningon control circuitry 404. For example, the application may be an EBIFapplication. In some embodiments, the application may be defined by aseries of JAVA-based files that are received and run by a local virtualmachine or other suitable middleware executed by control circuitry 404.

FIG. 5 shows a block diagram of illustrative network arrangement 500 forproviding recommendations, in accordance with some embodiments of thepresent disclosure. Illustrative system 500 may be representative ofcircumstances in which a user requests, views, listens to, browsesamong, creates, or otherwise interacts with content (e.g., originalcontent, base content, collaborative content, or a combination thereof).In system 500, there may be more than one type of user device, but onlyone is shown in FIG. 5 to avoid overcomplicating the drawing. Inaddition, each user may utilize more than one type of user device andalso more than one of each type of user device. User device 550 may bethe same as user device 400 of FIG. 4, user equipment system 401, anyother suitable device, or any combination thereof.

User device 550, illustrated as a wireless-enabled device, may becoupled to communications network 510 (e.g., connected to the Internet).For example, user device 550 is coupled to communications network 510via a communications path (e.g., which may include an access point). Insome embodiments, user device 550 may be a computing device coupled tocommunications network 510 via a wired connection. For example, userdevice 550 may also include wired connections to a LAN, or any othersuitable communications link to network 510. Communications network 510may be one or more networks including the Internet, a mobile phonenetwork, mobile voice or data network (e.g., a 4G or LTE network), cablenetwork, public switched telephone network, or other types ofcommunications network or combinations of communications networks.Communications paths may include one or more communications paths, suchas a satellite path, a fiber-optic path, a cable path, a path thatsupports Internet communications, free-space connections (e.g., forbroadcast or other wireless signals), or any other suitable wired orwireless communications path or combination of such paths. Althoughcommunications paths are not drawn between user device 550 and networkdevice 520, these devices may communicate directly with each other viacommunications paths, such as those described above, as well as othershort-range point-to-point communications paths, such as USB cables,IEEE 1394 cables, wireless paths (e.g., Bluetooth, infrared, IEEE802-11x, etc.), or other short-range communication via wired or wirelesspaths. BLUETOOTH is a certification mark owned by Bluetooth SIG, INC.Devices may also communicate with each other directly through anindirect path via communications network 510.

System 500, as illustrated, includes network device 520 (e.g., a serveror other suitable computing device) coupled to communications network510 via a suitable communications path. Communications between networkdevice 520 and user device 550 may be exchanged over one or morecommunications paths but are shown as a single path in FIG. 5 to avoidovercomplicating the drawing. Network device 520 may include a database(e.g., a recommendation database or any other suitable database), one ormore applications (e.g., as an application server, host server). Aplurality of network entities may exist and be in communication withnetwork 510, but only one is shown in FIG. 5 to avoid overcomplicatingthe drawing. In some embodiments, network device 520 may include onesource device. In some embodiments, network device 520 implements anapplication that communicates with instances of applications at manyuser devices (e.g., each one similar to user device 550). For example,an instance of a social media application may be implemented on userdevice 550, with application information being communicated to and fromnetwork device 520, which may store profile information for the user(e.g., so that a current social media feed is available on other devicesthan user device 550). In a further example, an instance of acollaboration application may be implemented on user device 550, withapplication information being communication to and from network device520, which may store profile information for the user, search historiesfrom a plurality of users, entity information (e.g., content andmetadata), tools for generating base content and collaborative content,any other suitable information, or any combination thereof.

In some embodiments, network device 520 includes one or more types ofstored information, including, for example, entity information,metadata, content, historical communications and search records, userpreferences, user profile information, any other suitable information,or any combination thereof. Network device 520 may include anapplications-hosting database or server, plug-ins, a software developerskit (SDK), an applications programming interface (API), or othersoftware tools configured to provide software (e.g., as downloaded to auser device), run software remotely (e.g., hosting applications accessedby user devices), or otherwise provide applications support toapplications of user device 550. In some embodiments, information fromnetwork device 520 is provided to user device 550 using a client/serverapproach. For example, user device 550 may pull information from aserver, or a server may push information to user device 550. In someembodiments, an application client residing on user device 550 mayinitiate sessions with network device 520 to obtain information whenneeded (e.g., when data is out-of-date or when a user device receives arequest from the user to receive data). In some embodiments, informationmay include user information (e.g., user profile information,user-created content). For example, the user information may includecurrent and/or historical user activity information such as what contenttransactions the user engages in, searches the user has performed,content the user has consumed/created, whether the user interacts with asocial network, any other suitable information, or any combinationthereof. In some embodiments, the user information may identify patternsof a given user for a period of time. As illustrated, network device 520includes entity information for a plurality of entities. Entityinformation 521, 522, and 523 include metadata for the respectiveentities. Entities for which metadata is stored in network device 520may be linked to each other, may be referenced to each other, may bedescribed by one or more tags in metadata, or a combination thereof.

In some embodiments, an application may be implemented on user device550, network device 520, or both. For example, the application may beimplemented as software or a set of executable instructions, which maybe stored in storage of the user device 550, network device 520, or bothand executed by control circuitry of the respective devices. In someembodiments, a collaboration application may include a searchapplication, a review application, a content analysis application, acontent creation application, a content modification application, or acombination thereof, that is implemented as a client/server-basedapplication, where only a client application resides on user device 550,and a server application resides on a remote server (e.g., networkdevice 520). For example, an application may be implemented partially asa client application on user device 550 (e.g., by control circuitry ofuser device 550) and partially on a remote server as a serverapplication running on control circuitry of the remote server (e.g.,control circuitry of network device 520). When executed by controlcircuitry of the remote server, the application may instruct the controlcircuitry to generate a display and transmit the generated display touser device 550. The server application may instruct the controlcircuitry of the remote device to transmit data for storage on userdevice 550. The client application may instruct control circuitry of thereceiving user device to generate the application displays.

In some embodiments, the arrangement of system 500 is a cloud-basedarrangement. The cloud provides access to services, such as informationstorage, searching, messaging, or social networking services, amongother examples, as well as access to any content described above, foruser devices. Services can be provided in the cloud throughcloud-computing service providers, or through other providers of onlineservices. For example, the cloud-based services can include a storageservice, a sharing site, a social networking site, a search engine, orother services via which user-sourced content is distributed for viewingby others on connected devices. These cloud-based services may allow auser device to store information to the cloud and to receive informationfrom the cloud rather than storing information locally and accessinglocally stored information. Cloud resources may be accessed by a userdevice using, for example, a web browser, a messaging application, asocial media application, a content interaction application, a desktopapplication, or a mobile application, and may include an audio recordingapplication, a speech-to-text application, a text-to-speech application,a voice-recognition application and/or any combination of accessapplications of the same. User device 550 may be a cloud client thatrelies on cloud computing for application delivery, or user device 550may have some functionality without access to cloud resources. Forexample, some applications running on user device 550 may be cloudapplications (e.g., applications delivered as a service over theInternet), while other applications may be stored and run on user device550. In some embodiments, user device 550 may receive information frommultiple cloud resources simultaneously.

FIGS. 6-8 include flowcharts of processes for providing recommendations.The illustrative process will be described in the context of arecommendation application, which may be implemented using any suitablecombination of hardware (e.g., user device 400, user equipment system401, network device 520, or user device 550) and software. For example,the recommendation application may include components and functionalityof, or otherwise be similar to, recommendation engines 120, 220, and 320of respective FIGS. 1-3.

FIG. 6 is a flowchart of illustrative process 600 for generating arecommendation of original content, in accordance with some embodimentsof the present disclosure. For example, a recommendation application mayperform process 600, implemented on any suitable hardware such as userdevice 400 of FIG. 4, user equipment system 401 of FIG. 4, user device550 of FIG. 5, network device 520 of FIG. 5, any other suitable device,or any combination thereof. In a further example, recommendation engine120 of FIG. 1 may perform some or all steps of process 600. Therecommendation application generates a recommendation of content forcollaboration. For example, popular, viral, or trending media contentitems may be recommended for users to interact with (e.g., collaborateon). The recommendation application gathers comments associated with acontent item, filters the comments to focus on relevant reviews, andthen determines if and how strongly to recommend the content item.Original content items may include songs, videos (e.g., with bothgraphics and audio), images, or any other content intended as finishedcontent. Original content may include previously uncollaborated-uponcontent, collaborative content, content modified from an original form,any other suitable content, or any combination thereof.

At step 602, the recommendation application identifies a first contentitem. In some embodiments, the first content item includes an originalcontent item. For example, the first content item may include a movie, amovie clip (e.g., a scene or trailer), a song (e.g., from an album,soundtrack, or streaming service), a song clip (e.g., a verse or sectionof instrumental), a musical composition (e.g., a symphony), soundeffects, a digital photograph, a graphic, any other suitable content, orany combination thereof. In some embodiments, the recommendationapplication receives (e.g., at a user interface) a user input indicatingor indicative of the original content. For example, the recommendationapplication may identify the original content based on the user input(e.g., the user keys in an identifier or selects an option). In someembodiments, the recommendation application identifies the originalcontent from among a plurality of original content items.

At step 604, the recommendation application retrieves a plurality ofreviews from one or more databases. In some embodiments, the pluralityof reviews are associated with the first content item. In someembodiments, metadata associated with the plurality of reviews includesprofile information associated with each review. For example, themetadata may include reviewer information (e.g., about a user or entitythat created the review), hashtags (e.g., or other tags that includekeywords), reactions to and interactions with the review, a time anddate of the review, a forum in which the review is posted, a rating(e.g., of the review or the reviewer), a ranking (e.g., of the review orthe reviewer), any other suitable information, or any combinationthereof. Reviews may include substantive comments, user recommendations,observances, or other user-generated commentary.

In some embodiments, the recommendation application performs step 604before step 602. For example, in some embodiments, the recommendationapplication identifies the original content from among a plurality oforiginal content, based on retrieved reviews. In a further example, therecommendation application may retrieve a plurality of reviews from aplurality of sources or forums, and identify the original content byidentifying references to the original content among the reviews.Identifying the first content item based on reviews allows, for example,the recommendation application to identify content for which there isexisting feedback.

At step 606, the recommendation application identifies one or morerecommendation criteria. In some embodiments, for example, therecommendation criteria may be retrieved from a database (e.g.,reference information 150 of FIG. 1) by the recommendation application.Recommendation criteria may include, for example, signal propertiesidentified as recommendation categories, positive or negative reviewtone, reviewer requirements (e.g., minimum ranking, rating, number ofreviews that user has generated, or relevance of the reviewer), anyother suitable criteria, or any combination thereof. In someembodiments, the recommendation criteria are predetermined and, forexample, are stored as a list. In some embodiments, the recommendationcriteria are selected based on metadata tags associated with the firstcontent item, reviews, reviewers, base content creators, or acombination thereof. In some embodiments, the recommendation criteriamay be based on the plurality of reviews. For example, if reviewsinclude the term “pitch” or “harmony,” the recommendation applicationmay use these as recommendation categories. The recommendationapplication may select a category for a property based on the number ofreviews referring to the property, which reviewer refers to theproperty, or whether the reviews are primarily positive or negative.

At step 608, the recommendation application filters the plurality ofreviews to generate a set of reviews. The recommendation application mayfilter reviews based on text of the respective reviews, profileinformation associated with the respective reviewers, review referenceinformation, any other suitable information, or any combination thereof.Filtering includes reducing the set of reviews under analysis based oncriteria. For example, an original set of N reviews retrieved fromsuitable sources may be reduced to a set of M reviews, where N is aninteger and where M is an integer less than N. To illustrate, therecommendation application may retrieve 1,000 user-generated commentsfrom a media content provider, and filter these reviews to 20-30 thatmention signal properties, include keywords, or have associated metadatatags that match reference tags.

In some embodiments, the recommendation application filters the reviewsby comparing text of the reviews to one or more references to generatematches. For example, the references may include predetermined keywordsor templates. The recommendation application identifies the set ofreviews based on the matches. For example, reviews that include keywordssuch as “pitch,” “tempo,” “harmony,” “better,” “if only,” “review,”“opinion,” “recommend,” or any other suitable keywords or phrases may beretained while those not including the keywords may be regarded asnon-review comments and need not be considered for generating arecommendation.

In some embodiments, the review reference information includes one ormore reference tags. In some such embodiments, the recommendationapplication filters the reviews by identifying one or more respectivemetadata tags associated with each the plurality of reviews, comparingthe one or more respective metadata tags to the reference tags. Forexample, reviews having metadata that includes tags such as “critique,”“review,” or any other suitable tags may be retained, while those forwhich the associated metadata does not include the keywords may beregarded as non-review comments and need not be considered forgenerating a recommendation.

At step 610, the recommendation application determines a recommendationmetric for the first content item based on the set of reviews and basedon the one or more recommendation criteria. The recommendation metric isindicative of whether the first content item is suitable for beingdesignated as base content for generating collaborative content. Therecommendation metric may include which properties to be changed fromthe first content item, whether and how strongly the first content itemis recommended, or both.

In some embodiments, the recommendation application determines therecommendation metric for the first content item by identifying ratinginformation of the set of reviews, wherein the rating information isindicative of users' responses to the first content item. For example,the rating information may include a binary rating (e.g., 0 or 1, goodor bad, useful or not useful), a scored rating (e.g., 99%, 4 out of 5stars, C-, any other score), a mapping (e.g., mapped with color, emojis,likes, symbols, or any other scale), a ranking (e.g., an index positionin a sorted list, an index, a grouping), any other suitable rating orgrading, or any combination thereof. For example, reviews that are morehighly rated may be associated with stronger recommendation metrics.

In some embodiments, the recommendation application determines therecommendation metric for the first content item by identifying a numberof reviews of the set of reviews that correspond to a firstrecommendation criterion. For example, the recommendation applicationmay recommend a content item more strongly for use in a particulargenre, or for users having particular profile information tags,especially if a large number of reviews are associated with those tags.In a further example, if a large number of reviews include text thatindicates the content could benefit from a modification to pitch, tempo,or modulation, then the recommendation application can recommend thecontent to a base content creator for those modifications (e.g., bycollaborators that may interact with the content).

In some embodiments, the recommendation application determines therecommendation metric for the first content item by identifying a numberof interactions with each review of the set of reviews by one or moreentities. For example, reviews that have more views, likes, replies,ratings, or otherwise are interacted with more, may be weighted moreheavily in the recommendation metric. To illustrate, if a review statinga song could use a duet at a certain portion of the song has a largenumber of likes, views, or reviews, then the recommendation applicationmay more strongly recommend that content as base content. In a furtherillustrative example, reviews having a large number of replies,regardless of positive or negative tone, may indicate popular content oropinions, which may in turn indicate content that is likely to becollaborated on (e.g., and is thus more strongly recommended).

In some embodiments, the recommendation application determines therecommendation metric for the first content item comprises determining aplurality of recommendation metrics, each associated with a respectiverecommendation criterion. For example, for a given content item, therecommendation metric may generate different recommendations for one ormore signal properties, review categories, use cases (e.g., how thecontent is likely to be consumed or collaborated on), user types, userprofile tags, for any other suitable category, or any combinationthereof.

At step 612, the recommendation application generates for output arecommendation indicator indicative of the recommendation metric. Insome embodiments, the recommendation application generates for outputthe recommendation indicator by generating for display (e.g., on adisplay device such as display 412) the recommendation indicator. Forexample, the recommendation application may generate for display on adisplay screen a list of content identifiers (e.g., corresponding to aplurality of content items including the first content item), graphics,selectable options, and suitable information, and display therecommendation indicator for one or more content identifiers (e.g., asillustrated in FIG. 1). In some embodiments, the recommendationapplication generates for output the recommendation indicator by storingthe recommendation indicator in metadata associated with the firstcontent item. For example, the recommendation application may generateone or more tags and store the tags in existing metadata associated withthe content item. In a further example, the recommendation indicator mayinclude a binary indicator (e.g., 0 or 1, yes or no), a scored indicator(e.g., 90%, 2 out of 5 stars, A+, any other score), a mapping (e.g.,mapped with color, emojis, likes, symbols, or any other scale), aranking (e.g., an index position in a sorted list, an index, agrouping), any other suitable rating or grading, or any combinationthereof. In some embodiments, the recommendation indicator includes oneor more tags of suggested modifications to the first content to createone or more versions of base content. For example, the recommendationindicator may include a tag indicating that the tempo of the firstcontent item should be slowed. Accordingly, the base content creator canalter the tempo of the first content item to generate the base content.Further, a plurality of recommendation indicators may be generated,corresponding to a plurality of recommended modifications to the firstcontent item to generate a plurality of corresponding base contents(e.g., each intended for differing audiences or collaborators).

FIG. 7 is a flowchart of illustrative process 700 for generating arecommendation of a media content item, in accordance with someembodiments of the present disclosure. For example, a recommendationapplication may perform process 700, implemented on any suitablehardware such as user device 400 of FIG. 4, user equipment system 401 ofFIG. 4, user device 550 of FIG. 5, network device 520 of FIG. 5, anyother suitable device, or any combination thereof. In a further example,recommendation engine 220 of FIG. 2 may perform some or all steps ofprocess 700. In some embodiments, recommendations of media content itemsare based on physical performance properties that characterize a mediacontent item and a user. Physical performance properties thatcharacterize a user include, for example, octave, pitch, adenoidalbehavior, tone, volume, accent, language, tempo, body movements,gestures, heart rate, body positions, body weight, body height, anysuitable characteristic that describes an activity that can beperformed, or any combination thereof. Physical performance propertiesthat characterize a media content item include physical performanceproperties of users captured by an electronically consumable mediacontent item and are not limited to what can be performed by a human,including digital modifications to what can be physically performed by ahuman (e.g., modulations for speech processing and filters for image andvideo processing). In some embodiments, the performance is recorded andstored in memory storage. Accordingly, the stored collaboration (e.g.,the user's performance and the media content item) may be processedaccordingly to any suitable process of the present disclosure (e.g.,process 600 of FIG. 6, process 800 of FIG. 8, or both)

At step 702, the recommendation application determines at least onephysical performance property from profile information associated with auser. In some embodiments, profile information includes information thatthe system uses to determine at least one physical performance property.For example, profile information may include a list of audio filesassociated with the user's voice (e.g., audio recordings of the usersinging) and a list of video files associated with the user's movements(e.g., videos of the user dancing). The audio and video files may beassociated with or stored in the user's profile for the recommendationapplication to process. In some embodiments, the physical performanceproperties of the user's voice and movements are determined by analyzingthese files. Analysis may include, for example, frequency analysis(e.g., determining frequencies or octave ranges the user's voice canreach), movement pattern recognition (e.g., recognizing that the user isperforming a head spin), any suitable digital signal processing of audioor video to determine a physical performance property, or anycombination thereof. In some embodiments, the profile informationincludes records of physical performance properties that are used todetermine additional physical performance properties. For example,profile information may include the user's height, weight, and bodymeasurements (e.g., waist measurement) that are then used by the systemto determine additional physical performance properties like body massindex. In some embodiments, the system determines the at least onephysical performance property by receiving the physical performanceproperty from a third-party source. For example, the system iscommunicatively coupled to an Internet of Things (IoT) wearable itemthat monitors heart rate, and a user's heart rate is received by thesystem to be associated with the user profile information as a physicalperformance property.

At step 704, the recommendation application compares at least onephysical performance property (e.g., from step 702) to a correspondingphysical performance property of a media content item. To illustrate,the recommendation application compares the user's performance to thatof the media content item. In some embodiments, the system analyzes atleast one physical performance property determined from profileinformation relative to a corresponding physical performance property ofeach of the plurality of media content items, wherein the profileinformation is associated with the user. The recommendation applicationmay compare a physical performance property determined at step 702 to acorresponding physical performance property of a media content item. Insome embodiments, the comparison produces an indication ofcompatibility. In some embodiments, the indication of compatibilityreflects an exact match. For example, body movements determined in step702 that the user's profile information reflects as movements the useris capable of match to the body movements required of the choreographyof a dance routine. In some embodiments, the indication of compatibilityreflects a degree or likelihood of matching. For example, the octaverange determined in step 702, which the profile information indicatesare musical notes that the user can sing, match 46 of the 50 musicalnotes that a song requires of its performer. In some embodiments, thecomparison of the at least one physical performance property to acorresponding physical performance property of a media content itemincludes determining the frequencies at which a physical performanceproperty is associated with the profile information. For example, for afigure skater whose recorded movement includes more instances ofsalchows than axels, the recommendation application maintains, as a partof the profile information, the frequency of each figure skating jumptype, each of which is considered a physical performance property (e.g.,the skating routine is characterized by physical performance propertiessuch as a triple axel), to determine that the salchow is more frequentlyassociated with the user profile than the axel. In some embodiments, thesystem uses the frequencies at which a physical performance property isexhibited in the profile information to determine, at step 706, whetherthe media content item is compatible for performance.

At step 705, the recommendation application determines an intent of theuser. For example, intent may be determined based on previous behavior,user input, how the input is provided, any other suitable criteria, orany combination thereof. In a further example, the recommendationapplication may include or apply any suitable functionality ofrecommendation engine 220 of FIG. 2, for example. In some embodiments,the recommendation application determines that the user has an intentionto perform, create content or both based on how the user accesses therecommendation application (e.g., using a shortcut on a user device). Insome embodiments, a link may be shared with the user (e.g., by anotheruser), and the recommendation application determines that because thelink is to join someone else to perform, the intention is to perform. Insome embodiments, the recommendation application identifies that a linkis provided for consuming content, and accordingly determines that theintention is to consume and not perform (e.g., to listen but not sing).In some embodiments, the recommendation application may identify userbehavior and determine an intent based on the behavior. For example, therecommendation application may determine by default that a content is tobe consumed (e.g., unless specific instructions are provided, orparticular devices are connected). In a further example, therecommendation application may determine an intent based on userlocation, based on a connected device, or based on other suitablecriteria. In some embodiments, the recommendation application uses thisdetermination for identifying which content to recommend forconsumption, collaboration, performance, or a combination thereof.

At step 706, the recommendation application determines whether the mediacontent item is compatible for performance by the user. In someembodiments, the recommendation application identifies the media contentitem as being compatible for performance by the user. The recommendationapplication may implement a predetermined threshold when determiningwhether the indication of compatibility confirms that a media contentitem is compatible for performance by the user. For example, apredetermined threshold for musical notes may be that “at least 90% ofthe musical notes required in a song must be within the user's singingabilities” for the song to be determined as compatible for the user toperform. In the illustrative example of 46 musical notes out of 50 totalmusical notes matching the user's singing capabilities, thepredetermined threshold is satisfied, and the system accordinglydetermines that song is compatible for the user to perform. If thesystem determines that the media content item is not compatible forperformance by the user, the system may return to step 702 (e.g., torestart the process of determining physical performance properties fromthe profile information). For example, in some embodiments, if newinformation defining the physical performance of a user is stored orassociated with the user profile since the last time the recommendationapplication has performed step 702, the recommendation applicationperforms steps 702 through 706 using the new information. If therecommendation application determines that the media content item iscompatible for performance by the user, the system proceeds to step 708.In some embodiments, the system assigns a weight to each of the at leastone physical performance properties and calculates a compatibility valueto determine compatibility for performance by the user. For example, thesystem assigns a larger weight to a physical performance propertyrepresenting the salchow movement than to a physical performanceproperty representing the axel movement because the salchow movementphysical performance property is more frequently associated with theuser profile information than the axel movement physical performanceproperty.

At step 708, the recommendation application generates for output arecommendation indicator of the media content item on a device. In someembodiments, the system presents a recommendation indicator ornotification of the media content item that has been determined to becompatible for the user to perform. For example, in some embodiments, adialogue box is displayed having a description of a song and aselectable soft button that, upon selection, allows the user to accessthe song. In some embodiments, the system generates for output aninvitation to access the recommended media content item. In anillustrative example, the system sends an invitation to a user (e.g., bymessage, SMS, email, or other notification) to play the base content ofthe compatible song for performance in a karaoke environment.

In an illustrative example, the recommendation application recommends asong to a singer. The singer's recorded songs (e.g., audio files) areanalyzed by the system. The system determines the physical performanceproperties of octave range and tempo by processing the audio files(e.g., taking a fast Fourier transform of the digitized signal todetermine the frequency response or using a series of comb filters todetermine the tempo of the audio file). In this illustrative example,the recommendation application determines that the singer has an octaverange from C3 to G5 and sings at a tempo of approximately 100 bpm (e.g.,average tempo of all audio files of the user singing). Therecommendation application compares the determined physical performanceproperties with the corresponding physical performance properties ofeach song in a library of songs. Based on the comparison, therecommendation application determines, in this example, compatible songsas having only notes within the singer's octave range and a tempoequivalent or slower than the average tempo. To illustrate, therecommendation application may be less likely to recommend MariahCarey's “Emotions” and more likely recommend Adele's “Hello” based onthe octave range physical performance property and tempo physicalperformance property of the two songs for that singer.

In a further illustrative example, the system recommends a dance routineto a dancer. Konami Corporation's video game Dance Dance Revolutioncontains a library of gaming levels where each level corresponds to aunique pattern of dance moves required to complete the level. A user'sprogress consuming the media content item of Dance Dance Revolution maybe associated with their profile information, which is accessible by therecommendation application (e.g., to recommend certain gaming levels ofDance Dance Revolution). The recorded dance movements of the user (e.g.,video files) and content consumption history (e.g., levels completed andrespective scores achieved) are analyzed by the recommendationapplication. The recommendation application determines, for example, thephysical performance properties of tempo and cross step frequency basedon profile information. The recommendation application compares thedetermined physical performance properties associated with the userprofile information to the physical performance properties associatedwith the physical performance properties of each level of Dance DanceRevolution. The recommendation application recommends a level thatmatches the user's average cross step frequency and the average tempo towhich the user dances.

In a further illustrative example, the recommendation applicationrecommends an exercise type to an athlete. In some embodiments, therecommendation application analyzes physical performance properties ofaverage heart rate during exercise, body weight, body height, and agethat are determined from the athlete's profile information. The physicalperformance properties of various exercises such as, for example,swimming, weight lifting, and interval training are compared to thephysical performance properties determined from the profile informationassociated with the athlete. To illustrate, the recommendationapplication may identify swimming as an exercise that is compatible forthe user's average heart rate during exercise and current state ofphysical fitness determined from age, height, and weight. The systemprovides a recommendation to the athlete on the athlete's mobile device(e.g., a smartphone), for example.

Process 700, which is applicable to a single user, may be applied to asecond user (e.g., or multiple users) such that the media content itemrecommended is compatible for both users (e.g., or a group of users)based on respective profile information. For example, the recommendationapplication recommends the same song to two singers to sing togetherbased on an analysis of the octave range of the song and the singers'octave ranges. The system may determine a duet is compatible for twosingers and recommend the duet to them, for which certain parts of thesong are compatible for the first singer's physical performanceproperties and other parts of the song are compatible for the secondsinger's physical performance properties. For example, “Endless Love” byDiana Ross and Lionel Richie is recommended to two singers because theoctave range physical performance properties of the two singers matchwith the octave range physical performance properties of Ross andRichie, respectively. In some embodiments, the system generatesrecommendations of portions of the recommended media content item, whereeach recommended portion is designated for the user that the portion iscompatible with. For example, the system recommends only Ross's portionsof “Endless Love” to a first of two singers and recommends only Richie'sportions of the song to the second of the two singers. In someembodiments, the system generates for output recommendations to theusers' devices separately (e.g., a first recommendation to a firstdevice and a second recommendation to a second device) according to theportions of the media content item determined to be compatible with theuser profiles' respective physical performance properties. For example,the recommendation application sends the recommended Ross portions of“Endless Love” to the first singer's smartphone and sends therecommended Richie portions of the song to the second singer'ssmartphone. Similar recommendations can be made for group dances (e.g.,a group dance routine featuring styles of hip hop, break dancing, andwaacking) and group exercises (e.g., swimming medley relays). In someembodiments, content recommendations generated by process 600 of FIG. 6may include tags specifying compatible or preferred profile information(e.g., which properties a user exhibits that may be compatible with agiven base content item).

In some embodiments, the recommendation application analyzes contentitem consumption history associated with the profile information toidentify a media content item to recommend. For example, therecommendation application analyzes a list of songs with which a userhas previously performed karaoke, identifies a song among the previouslyperformed songs that is compatible with the at least one physicalperformance property, and recommends the song. In some embodiments, thesystem receives the user's content item consumption history associatedwith the profile information from a third-party application. Forexample, the list of songs that the user has previously performedkaraoke with is maintained by the mobile application Smule.

FIG. 8 is a flowchart of illustrative process 800 for generating arecommendation of collaborative content, in accordance with someembodiments of the present disclosure. For example, a recommendationapplication may perform process 800, implemented on any suitablehardware such as user device 400 of FIG. 4, user equipment system 401 ofFIG. 4, user device 550 of FIG. 5, network device 520 of FIG. 5, anyother suitable device, or any combination thereof. In a further example,recommendation engine 320 of FIG. 3 may perform some or all steps ofprocess 800. In some embodiments, collaborative content may be generatedusing process 700 of FIG. 7, wherein the performance is recorded anddigitally stored (e.g., as a video, audio, or image file).

At step 802, the recommendation application identifies a base contentitem. The base content may include video content (e.g., a sequence ofimages, an animation, cinema), audio content (e.g., a song, aninstrumental, speech, sound effects), image content (e.g., digitalphotos, animations, graphics, slides, digitized drawings), any othersuitable content, or any combination thereof. In some circumstances,content may include video content and audio content (e.g., a musicvideo). In some circumstances, video content may include image contentsuch as a sequence of images, each identifiable and suitable foranalysis.

At step 804, the recommendation application identifies a collaborativecontent item. The collaborative content may include video content, audiocontent, image content, any other suitable content, or any combinationthereof. The collaborative content includes both the base content andadditional content combined with the base content. In some embodiments,profile information is associated with the collaborative content. Forexample, the profile information may include information associated witha content creator, the content itself, or a combination thereof. In someembodiments, the recommendation application receives (e.g., at a userinterface) a user input indicating or indicative of the collaborativecontent. For example, the recommendation application may identify thecollaborative content based on the user input (e.g., the user keys in anidentifier or selects an option).

At step 806, the recommendation application analyzes collaborativecontent to determine one or more signal properties of the collaborativecontent. In some embodiments, for which the collaborative contentincludes collaborative audio content, signal properties may include, forexample, pitch, octave, tone, tempo, key, harmony, rhythm, volume,modulation, any other suitable property, or any combination thereof.Signal properties include, for example, properties that can bedetermined based on a signal that includes the content data. Forexample, by analyzing properties of the signal, aspects of the signalcan be characterized, qualified, or quantified.

At step 808, the recommendation application determines a recommendationmetric of the collaborative content based at least in part on the one ormore signal properties and based at least in part on profileinformation.

In some embodiments, the recommendation application analyzes thecollaborative content to determine one or more metrics corresponding tothe one or more signal properties. In some such embodiments, therecommendation application compares one or more determined metrics tothe one or more reference metrics to generate the recommendation metric.

In an illustrative example, for audio content, the recommendationapplication may determine a pitch metric (e.g., scored on how high orlow one or more notes is), octave (e.g., scored on which octave(s) thecontent lies in), tone (e.g., scored on how steady the tone is), tempo(e.g., scored on the speed or feature timing), key (e.g., scored oncomparison with a reference or benchmarks), harmony (e.g., scored onphasing or relative time signatures of features), rhythm (e.g., scoredon regularity and timing), volume (e.g., scored on volume level andrange in volume), modulation (e.g., scored on timing, extent ofmodulation, modulation features compared to reference features), anyother suitable metric, or any combination thereof.

In an illustrative example, for video content, the recommendationapplication may determine a color metric (e.g., scored on palette, colorscale), brightness (e.g., scored on local or overall brightness),lighting (e.g., scored on directionality, diffusivity, specularity),motion (e.g., scored on the speed, timing, harmony, or extent ofmotion), accompanying audio (e.g., scored on any of the metricsdiscussed above), playback (e.g., scored on framerate, noise,distortion), any other suitable metric, or any combination thereof.

In an illustrative example, for image content, the recommendationapplication may determine a color metric (e.g., scored on palette, colorscale), brightness (e.g., scored on local or overall brightness),lighting (e.g., scored on directionality, diffusivity, specularity),composition (e.g., scored on the arrangement of features), any othersuitable metric, or any combination thereof.

In an illustrative example, the recommendation application may determinethe recommendation metric of the collaborative content by analyzing thecollaborative content to determine one or more metrics corresponding tothe one or more signal properties, and then comparing the one or moremetrics to one or more reference metrics. In some embodiments, therecommendation application determines the one or more reference metricsbased on analyzing the corresponding one or more base signal propertiesof the base content. For example, the recommendation application maydetermine a pitch metric based on the collaboration content and areference pitch metric based on the base content, and then compare thepitch metric to the reference pitch metric. The recommendation metricmay be inversely proportional to a difference or variation between themetric and the reference metric. For example, the closer the signalproperty, or metric thereof, of the collaborative content is to thesignal property, or metric thereof, of the base content, the strongerthe recommendation metric (e.g., a larger numerical value and thus“more” recommended).

In a further illustrative example, the recommendation application maydetermine the recommendation metric of the collaborative content basedon ratings. In some embodiments, the recommendation applicationdetermines a first rating associated with the base content and a secondrating associated with the collaborative content. In some suchembodiments, the recommendation application compares the first rating tothe second rating to determine the recommendation metric. For example,if the first rating is high, then a high second rating will result in agreater recommendation metric and a low second rating will result in alesser recommendation metric. Accordingly, the recommendationapplication may recommend collaborations that are rated similarly to thebase content. In some embodiments, the first rating is a fixed constantor null, thus making the base content rating irrelevant for comparison.In some such embodiments, the recommendation application determines therecommendation metric based on the second rating alone.

In a further illustrative example, the recommendation application maydetermine the recommendation metric based on content formats. In someembodiments, the recommendation application determines a first formatassociated with the base content and a second format associated with thecollaborative content. In some such embodiments, the recommendationapplication compares the first format and the second format to generatethe recommendation metric. For example, if the base content includesvideo content with audio content (e.g., such as a music video), therecommendation application may recommend collaborative content thatincludes both video and audio content rather than one or the other. Toillustrate, if the base content is a music video, then collaborativecontent that includes a video with no sound, or an audio track with novideo, will have a lesser recommendation than a collaboration havingboth video and sound. In a further example, the recommendationapplication may recommend collaborative content having a similar filetype or file format as the base content (e.g., both may be MPEGS, WAVfiles, or JPEG files).

In some embodiments, the recommendation application combines a pluralityof recommendation metrics, each determined based on a particularcriterion, to generate a resulting recommendation metric. For example,the recommendation application may use a sum, an average, a weightedsum, a weighted average, a product, any other operation or series ofoperations, or any combination thereof. For example, a plurality ofrecommendation metrics may be determined, each ranging from 0-1 (or eachranging in any other suitable interval), and the recommendationapplication may determine the product of the recommendation metrics asthe resulting recommendation metric.

At step 810, the recommendation application generates for output arecommendation indicator indicative of the recommendation metric. Insome embodiments, the recommendation application stores therecommendation indicator in metadata associated with the collaborativecontent. For example, the metadata may be stored in suitable memorystorage of a network device or user device. In a further example, therecommendation application may generate a tag and store the tag as partof existing metadata. A recommendation indicator may include a binaryindicator (e.g., 0 or 1, yes or no, highly recommended or minimallyrecommended), a scored indicator (e.g., 80%, 4 out of 5 stars, B+, anyother score), a mapping (e.g., mapped with color, emojis, symbols, orany other scale), a ranking (e.g., an index position in a sorted list,an index, a grouping), any other suitable recommendation indicator, orany combination thereof. For example, for a highly recommendedcollaborative content item, the recommendation application may output atag including the value, symbol identifier, grading, score, ranking,mapping, or other suitable tag.

At step 812, the recommendation application generates for display on adisplay device the recommendation indicator. In some embodiments, therecommendation application may generate for display a graphiccorresponding to the recommendation indicator. For example, therecommendation application may generate for display on a display screena list of content identifiers, graphics, selectable options, andsuitable information, and display the recommendation indicator for oneor more content identifiers (e.g., as illustrated in FIG. 3). In someembodiments, steps 810 and 812 may be combined, or either may beomitted, in accordance with the present disclosure. For example, therecommendation application may generate for display the recommendationindicator, and otherwise need not output to storage. In a furtherexample, the recommendation application may store the recommendationindicator, but the indicator is not displayed.

It is contemplated that the steps or descriptions of FIGS. 6-8 may beused with any other embodiment of this disclosure. In addition, thesteps and descriptions described in relation to FIGS. 6-8 may be done inalternative orders or in parallel to further the purposes of thisdisclosure. For example, each of these steps may be performed in anyorder or in parallel or substantially simultaneously to reduce lag orincrease the speed of the system or method. Any of these steps may alsobe skipped or omitted from the respective processes. Furthermore, itshould be noted that any of the devices or equipment discussed inrelation to FIGS. 4-5, a suitable network entity (e.g., a server), asuitable user device (e.g., a smartphone), or a combination thereof,could be used to perform one or more of the steps in FIGS. 6-8.Furthermore, it should be noted that the features and limitationsdescribed in any one embodiment may be applied to any other embodimentherein, and flowcharts or examples relating to one embodiment may becombined with any other embodiment in a suitable manner, done indifferent orders, performed with addition steps, performed with omittedsteps, or done in parallel. In addition, the systems and methodsdescribed herein may be performed in real time. It should also be notedthat the systems and/or methods described above may be applied to, orused in accordance with, other systems and/or methods.

In an illustrative example, a recommendation application may identifyoriginal content and recommend that content to a base content creator(e.g., process 600), identity base content and recommend that basecontent to a user for interacting with or collaborating on (e.g.,process 700), identify a collaboration and recommend that collaborationto one or more users for consumption (e.g., process 800), or acombination thereof.

The above-described embodiments of the present disclosure are presentedfor purposes of illustration and not of limitation, and the presentdisclosure is limited only by the claims that follow. Furthermore, itshould be noted that the features and limitations described in any oneembodiment may be applied to any other embodiment herein, and flowchartsor examples relating to one embodiment may be combined with any otherembodiment in a suitable manner, done in different orders, or done inparallel. In addition, the systems and methods described herein may beperformed in real time. It should also be noted, the systems and/ormethods described above may be applied to, or used in accordance with,other systems and/or methods.

1-30. (canceled)
 31. A method comprising: identifying a base content;identifying, using control circuitry, a plurality of collaborativecontent, wherein each collaborative content of the plurality ofcollaborative content comprises the base content and an additionalcontent combined with the base content; determining, using the controlcircuitry, a recommendation metric of each collaborative content of theplurality of collaborative content based at least in part on one or moresignal properties and based at least in part on profile information;filtering, using the control circuitry, the plurality of collaborativecontent based on: 1) the recommendation metric of each collaborativecontent of the plurality of collaborative content, and 2) the profileinformation associated with each collaborative content of the pluralityof collaborative content; generating a recommendation indicator based onthe recommendation metric and the profile information; and generatingfor output, using control circuitry, the recommendation indicator. 32.The method of claim 31, wherein the base content comprises base audiocontent, and wherein each collaborative content of the plurality ofcollaborative content comprises collaborative audio content.
 33. Themethod of claim 31, wherein the base content comprises base videocontent, and wherein each collaborative content of the plurality ofcollaborative content comprises collaborative video content.
 34. Themethod of claim 31, wherein determining the recommendation metric ofeach collaborative content of the plurality of collaborative contentcomprises: analyzing the each collaborative content of the plurality ofcollaborative content to determine one or more metrics corresponding tothe one or more signal properties; and comparing the one or more metricsto a one or more reference metrics to generate the recommendationmetric.
 35. The method of claim 34, further comprising determining theone or more reference metrics that correspond to the one or more basesignal properties of the base content.
 36. The method of claim 31,wherein determining the recommendation metric of each collaborativecontent of the plurality of collaborative content comprises: determininga first rating associated with the base content; determining a secondrating associated with each collaborative content of the plurality ofcollaborative content; and comparing the first rating and the secondrating to generate the recommendation metric.
 37. The method of claim31, wherein determining the recommendation metric of each collaborativecontent of the plurality of collaborative content comprises: determininga first format associated with the base content; determining a secondformat associated with the each collaborative content of the pluralityof collaborative content; and comparing the first format and the secondformat to generate the recommendation metric.
 38. The method of claim31, wherein the base content comprises base audio content, wherein theeach collaborative content of the plurality of collaborative contentcomprises collaborative audio content, and wherein the one or moresignal properties comprise at least one selected from the groupcomprising pitch, octave, tone, tempo, key, harmony, rhythm, volume, andmodulation.
 39. The method of claim 31, further comprising receiving, ata user interface, a user input indicative of a first collaborativecontent of the plurality of collaborative content, wherein identifyingthe first collaborative content comprises identifying the firstcollaborative content based on the user input.
 40. The method of claim31, wherein generating for output the recommendation indicator comprisesstoring the recommendation indicator in metadata associated with eachcollaborative content of the plurality of collaborative content.
 41. Asystem comprising: a display device; and control circuitry coupled tothe display device, and configured to: identify a base content;identify, using control circuitry, a plurality of collaborative content,wherein each collaborative content of the plurality of collaborativecontent comprises the base content and an additional content combinedwith the base content; determine, using the control circuitry, arecommendation metric of each collaborative content of the plurality ofcollaborative content based at least in part on one or more signalproperties and based at least in part on profile information; filter,using the control circuitry, the plurality of collaborative contentbased on: 1) the recommendation metric of each collaborative content ofthe plurality of collaborative content, and 2) the profile informationassociated with each collaborative content of the plurality ofcollaborative content; generate a recommendation indicator based on therecommendation metric and the profile information; and generate foroutput, using control circuitry, the recommendation indicator.
 42. Thesystem of claim 41, wherein the base content comprises base audiocontent, and wherein each collaborative content of the plurality ofcollaborative content comprises collaborative audio content.
 43. Thesystem of claim 41, wherein the base content comprises base videocontent, and wherein each collaborative content of the plurality ofcollaborative content comprises collaborative video content.
 44. Thesystem of claim 41, wherein the control circuitry is further configuredto determine the recommendation metric of each collaborative content ofthe plurality of collaborative content by: analyzing each collaborativecontent of the plurality of collaborative content to determine one ormore metrics corresponding to the one or more signal properties; andcomparing the one or more metrics to a one or more reference metrics togenerate the recommendation metric.
 45. The system of claim 44, whereinthe control circuitry is further configured to determine the one or morereference metrics that correspond to the one or more base signalproperties of the base content.
 46. The system of claim 41, wherein thecontrol circuitry is further configured to determine the recommendationmetric of each collaborative content of the plurality of collaborativecontent by: determining a first rating associated with the base content;determining a second rating associated with each collaborative contentof the plurality of collaborative content; and comparing the firstrating and the second rating to generate the recommendation metric. 47.The system of claim 41, wherein the control circuitry is furtherconfigured to determine the recommendation metric of each collaborativecontent of the plurality of collaborative content by: determining afirst format associated with the base content; determining a secondformat associated with each collaborative content of the plurality ofcollaborative content; and comparing the first format and the secondformat to generate the recommendation metric.
 48. The system of claim41, wherein the base content comprises base audio content, wherein eachcollaborative content of the plurality of collaborative contentcomprises collaborative audio content, and wherein the one or moresignal properties comprise at least one selected from the groupcomprising pitch, octave, tone, tempo, key, harmony, rhythm, volume, andmodulation.
 49. The system of claim 41, further comprising a userinterface coupled to the control circuitry and configured to receiveuser input, wherein the control circuitry is further configured toidentify collaborative content of the plurality of collaborative contentcomprises identifying a first collaborative content based on the userinput.
 50. The system of claim 41, wherein the control circuitry isfurther configured to store the recommendation indicator in metadataassociated with a first collaborative content of the plurality ofcollaborative content.